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"""Example plot showing the profile of the Cash statistic and its connection to excess errors."""
import numpy as np
import matplotlib.pyplot as plt
from gammapy.stats import CashCountsStatistic
count_statistic = CashCountsStatistic(n_on=13, mu_bkg=5.5)
excess = count_statistic.n_sig
errn = count_statistic.compute_errn(1.0)
errp = count_statistic.compute_errp(1.0)
errn_2sigma = count_statistic.compute_errn(2.0)
errp_2sigma = count_statistic.compute_errp(2.0)
# We compute the Cash statistic profile
mu_signal = np.linspace(-1.5, 25, 100)
stat_values = count_statistic._stat_fcn(mu_signal)
xmin, xmax = -1.5, 25
ymin, ymax = -42, -28.0
plt.figure(figsize=(5, 5))
plt.plot(mu_signal, stat_values, color="k")
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
plt.xlabel(r"Number of expected signal event, $\mu_{sig}$")
plt.ylabel(r"Cash statistic value, TS ")
plt.hlines(
count_statistic.stat_max + 1,
xmin=excess - errn,
xmax=excess + errp,
linestyle="dotted",
color="r",
label="1 sigma (68% C.L.)",
)
plt.vlines(
excess - errn,
ymin=ymin,
ymax=count_statistic.stat_max + 1,
linestyle="dotted",
color="r",
)
plt.vlines(
excess + errp,
ymin=ymin,
ymax=count_statistic.stat_max + 1,
linestyle="dotted",
color="r",
)
plt.hlines(
count_statistic.stat_max + 4,
xmin=excess - errn_2sigma,
xmax=excess + errp_2sigma,
linestyle="dashed",
color="b",
label="2 sigma (95% C.L.)",
)
plt.vlines(
excess - errn_2sigma,
ymin=ymin,
ymax=count_statistic.stat_max + 4,
linestyle="dashed",
color="b",
)
plt.vlines(
excess + errp_2sigma,
ymin=ymin,
ymax=count_statistic.stat_max + 4,
linestyle="dashed",
color="b",
)
plt.legend()
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